Atom-by-atom simulations of chemical vapor deposition of nanoporous hydrogenated silicon nitride
Bibliographic record
Abstract
Amorphous hydrogenated silicon nitride (SiNH) materials prepared by plasma-enhanced chemical vapor deposition (PECVD) are of high interest because of their suitability for diverse applications including optical coatings, gas/vapor permeation barriers, corrosion resistant, and protective coatings and numerous others. In addition, they are very suitable for structurally graded systems such as those with a graded refractive index. In parallel, modeling the PECVD process of SiN(H) of an a priori given SiN(H) ratio by atomistic calculations represents a challenge due to: (1) different (and far from constant) sticking coefficients of individual elements, and (2) expected formation of N2 (and H2) gas molecules. In the present work, we report molecular-dynamics simulations of particle-by-particle deposition process of SiNH films from SiHx and N radicals. We observe formation of a mixed zone (damaged layer) in the initial stages of film growth, and (under certain conditions) formation of nanopores in the film bulk. We investigate the effect of various PECVD process parameters (ion energy, composition of the SiHx+N particle flux, ion fraction in the particle flux, composition of the SiHx radicals, angle of incidence of the particle flux) on both (1) deposition characteristics, such as sticking coefficients, and (2) material characteristics, such as dimension of the nanopores formed. The results provide detailed insight into the complex relationships between these process parameters and the characteristics of the deposited SiNH materials and exhibit an excellent agreement with the experimentally observed results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".